Neural Generativity: Frontiers Of Artificial Creativity
DOI:
https://doi.org/10.15680/IJCTECE.2018.0102001Keywords:
Generative Models, Artificial Creativity, Neural Networks, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformer Models, Artificial Intelligence, Ethical AI, AI Art, Neural CreativityAbstract
The field of artificial intelligence (AI) has reached new frontiers with the rise of generative models that simulate human-like creativity. These models are capable of producing highly realistic and original outputs across a variety of domains, including visual art, music, literature, and more. This paper explores the evolution of generative models, particularly focusing on neural networks such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformer-based models like GPT. These technologies have revolutionized not only AI research but also industries such as entertainment, design, and healthcare. The paper delves into the mechanisms behind these models, including their underlying architectures and training processes, and analyzes their creative potential and limitations. It further examines the ethical and societal implications of AI-generated content, addressing concerns regarding bias, misinformation, and intellectual property. By discussing the current state of generative models and their potential for future advancements, this paper presents a comprehensive understanding of how neural networks are reshaping the concept of creativity in the digital age.
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